A study has been made for a technique of calculating a degree of fatigue of a PC (Personal Computer) by measuring a vibration or temperature of the PC. There has also been proposed an apparatus that acquires information of a PC by a sensor and presents to a customer a probability of trouble occurrence for an individual component or for the PC. In this technique, a model is constructed, which is for monitoring whether operation data of a product has a suitable value that does not lead to failure/breakdown, and whether the product is going to be broken or not is evaluated with the use of the model. In a quality control section of hardware, a technique of evaluating a rate of repair of a product from repair data has been accumulated.
As techniques of evaluating a quality of a product, an analysis of service-life data, which takes censored observation of a product into consideration, and a semi-supervised learning have been known. In the analysis of the service-life data taking the censoring into consideration, information, which indicates that the product operated without being broken at the time when the observation was censored, is used for censored observation data. This analysis method can be used for the construction of a failure model with respect to an accumulated load, such as a service life with respect to an accumulated operation time. However, it cannot be used for the construction of a failure model with respect to an observation item, such as a temperature or acceleration, which is not an accumulated value.
The semi-supervised learning is a machine learning technique using both of labeled data and unlabeled data. For example, a technique of K. Nigam and A. McCallum and S. Thrun and T. Mitchell, “Learning to Classify Text from Labeled and Unlabeled Documents”, Proc. of the 15th National Conf. on Artificial Intelligence, pp. 792-799, 1998 has been known.
In the case of using a model as mentioned above, when the number of failure data pieces and non-failure data pieces are small (e.g., when the product has just been shipped out), it is difficult to construct a high-precise model. Furthermore, there may be a case in which operation data, which cannot be classified into failure or non-failure, is present, because of the discontinuation of the observation.